{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.386290Z",
     "start_time": "2020-11-17T13:34:44.377670Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.391954Z",
     "start_time": "2020-11-17T13:34:44.388521Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Compare between cell line prediction and cell type-specific prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.397153Z",
     "start_time": "2020-11-17T13:34:44.394642Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.402636Z",
     "start_time": "2020-11-17T13:34:44.399952Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.407536Z",
     "start_time": "2020-11-17T13:34:44.404538Z"
    }
   },
   "outputs": [],
   "source": [
    "# log2_median_ic50, log2_median_ic50_9f, log2_median_ic50_hn, log2_median_ic50_9f_hn, log2_median_ic50_3f_hn, log2_max_conc\n",
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'RWEN'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.412155Z",
     "start_time": "2020-11-17T13:34:44.409581Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/patient_TPM/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.426826Z",
     "start_time": "2020-11-17T13:34:44.414677Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.554681Z",
     "start_time": "2020-11-17T13:34:44.429767Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.562384Z",
     "start_time": "2020-11-17T13:34:44.557281Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Not combined replicate #####\n",
    "\n",
    "# validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "# cv_single_df_list = []\n",
    "# cv_combi_df_list = []\n",
    "\n",
    "# for validation_fname in validation_fname_list:\n",
    "\n",
    "#     cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "#     cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_single_df = cv_single_df.reset_index()\n",
    "\n",
    "#     cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "#     cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "#     cv_combi_df = cv_combi_df.reset_index()\n",
    "    \n",
    "#     cv_single_df_list += [cv_single_df]\n",
    "#     cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "# cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "# cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.569292Z",
     "start_time": "2020-11-17T13:34:44.564279Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159']\n",
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103']\n",
    "\n",
    "# single_drug_id_list = [1007, 133, 1010, 182, 301, 302]\n",
    "# single_drug_list = ['Docetaxel', 'Doxorubicin', 'Gefitinib', 'Obatoclax Mesylate', 'PHA-793887', 'PI-103']\n",
    "# combi_drug_list = ['Docetaxel|Gefitinib']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.582196Z",
     "start_time": "2020-11-17T13:34:44.571532Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.586557Z",
     "start_time": "2020-11-17T13:34:44.583809Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "##### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Preprocess predicted and observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.598758Z",
     "start_time": "2020-11-17T13:34:44.588520Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.616131Z",
     "start_time": "2020-11-17T13:34:44.600363Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>47.25</td>\n",
       "      <td>34.18</td>\n",
       "      <td>55.59</td>\n",
       "      <td>73.87</td>\n",
       "      <td>3.39</td>\n",
       "      <td>27.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44.07</td>\n",
       "      <td>28.62</td>\n",
       "      <td>58.39</td>\n",
       "      <td>73.48</td>\n",
       "      <td>5.57</td>\n",
       "      <td>30.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48.24</td>\n",
       "      <td>31.68</td>\n",
       "      <td>57.19</td>\n",
       "      <td>74.51</td>\n",
       "      <td>4.97</td>\n",
       "      <td>30.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>49.10</td>\n",
       "      <td>61.99</td>\n",
       "      <td>31.87</td>\n",
       "      <td>35.45</td>\n",
       "      <td>26.92</td>\n",
       "      <td>56.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45.15</td>\n",
       "      <td>58.86</td>\n",
       "      <td>36.26</td>\n",
       "      <td>29.41</td>\n",
       "      <td>26.08</td>\n",
       "      <td>54.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.05</td>\n",
       "      <td>58.84</td>\n",
       "      <td>32.00</td>\n",
       "      <td>32.61</td>\n",
       "      <td>25.09</td>\n",
       "      <td>58.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>67.53</td>\n",
       "      <td>63.16</td>\n",
       "      <td>50.80</td>\n",
       "      <td>32.72</td>\n",
       "      <td>24.21</td>\n",
       "      <td>51.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>64.43</td>\n",
       "      <td>62.38</td>\n",
       "      <td>54.29</td>\n",
       "      <td>29.53</td>\n",
       "      <td>23.48</td>\n",
       "      <td>52.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67.12</td>\n",
       "      <td>61.73</td>\n",
       "      <td>53.64</td>\n",
       "      <td>34.09</td>\n",
       "      <td>23.81</td>\n",
       "      <td>54.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>55.98</td>\n",
       "      <td>71.19</td>\n",
       "      <td>36.99</td>\n",
       "      <td>43.62</td>\n",
       "      <td>20.77</td>\n",
       "      <td>78.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          47.25  34.18   \n",
       "                                                   2          44.07  28.62   \n",
       "                                                   3          48.24  31.68   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          49.10  61.99   \n",
       "                                                   2          45.15  58.86   \n",
       "                                                   3          50.05  58.84   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          67.53  63.16   \n",
       "                                                   2          64.43  62.38   \n",
       "                                                   3          67.12  61.73   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          55.98  71.19   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          55.59  73.87   \n",
       "                                                   2          58.39  73.48   \n",
       "                                                   3          57.19  74.51   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          31.87  35.45   \n",
       "                                                   2          36.26  29.41   \n",
       "                                                   3          32.00  32.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          50.80  32.72   \n",
       "                                                   2          54.29  29.53   \n",
       "                                                   3          53.64  34.09   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          36.99  43.62   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           3.39  27.10  \n",
       "                                                   2           5.57  30.12  \n",
       "                                                   3           4.97  30.80  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          26.92  56.40  \n",
       "                                                   2          26.08  54.51  \n",
       "                                                   3          25.09  58.38  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          24.21  51.81  \n",
       "                                                   2          23.48  52.38  \n",
       "                                                   3          23.81  54.06  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          20.77  78.44  "
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.640801Z",
     "start_time": "2020-11-17T13:34:44.617665Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.651395Z",
     "start_time": "2020-11-17T13:34:44.642435Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>52.63</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>89.68</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>92.86</td>\n",
       "      <td>Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>22.20</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>76.48</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  patient  drug_id   kill           drug_name\n",
       "0   HN120     1007  52.63           Docetaxel\n",
       "1   HN120      133  89.68         Doxorubicin\n",
       "2   HN120      201  92.86        Epothilone B\n",
       "3   HN120     1010  22.20           Gefitinib\n",
       "4   HN120      182  76.48  Obatoclax Mesylate"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.683975Z",
     "start_time": "2020-11-17T13:34:44.653634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>kill_C</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>74.70</td>\n",
       "      <td>68.43</td>\n",
       "      <td>74.70</td>\n",
       "      <td>68.43</td>\n",
       "      <td>92.01</td>\n",
       "      <td>17.31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>6.27</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>51.80</td>\n",
       "      <td>32.12</td>\n",
       "      <td>51.80</td>\n",
       "      <td>32.12</td>\n",
       "      <td>67.28</td>\n",
       "      <td>15.49</td>\n",
       "      <td>0.30</td>\n",
       "      <td>19.67</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>77.47</td>\n",
       "      <td>81.65</td>\n",
       "      <td>77.47</td>\n",
       "      <td>81.65</td>\n",
       "      <td>95.86</td>\n",
       "      <td>14.22</td>\n",
       "      <td>0.17</td>\n",
       "      <td>4.18</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>25.63</td>\n",
       "      <td>18.09</td>\n",
       "      <td>25.63</td>\n",
       "      <td>18.09</td>\n",
       "      <td>39.08</td>\n",
       "      <td>13.46</td>\n",
       "      <td>0.53</td>\n",
       "      <td>7.54</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>42.14</td>\n",
       "      <td>21.89</td>\n",
       "      <td>42.14</td>\n",
       "      <td>21.89</td>\n",
       "      <td>54.81</td>\n",
       "      <td>12.67</td>\n",
       "      <td>0.30</td>\n",
       "      <td>20.24</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>52.35</td>\n",
       "      <td>23.58</td>\n",
       "      <td>52.35</td>\n",
       "      <td>23.58</td>\n",
       "      <td>63.58</td>\n",
       "      <td>11.24</td>\n",
       "      <td>0.21</td>\n",
       "      <td>28.77</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>52.63</td>\n",
       "      <td>22.20</td>\n",
       "      <td>52.63</td>\n",
       "      <td>22.20</td>\n",
       "      <td>63.14</td>\n",
       "      <td>10.52</td>\n",
       "      <td>0.20</td>\n",
       "      <td>30.43</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>42.14</td>\n",
       "      <td>77.47</td>\n",
       "      <td>42.14</td>\n",
       "      <td>77.47</td>\n",
       "      <td>86.96</td>\n",
       "      <td>9.49</td>\n",
       "      <td>0.12</td>\n",
       "      <td>35.33</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>HN182</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>86.77</td>\n",
       "      <td>70.95</td>\n",
       "      <td>86.77</td>\n",
       "      <td>70.95</td>\n",
       "      <td>96.16</td>\n",
       "      <td>9.39</td>\n",
       "      <td>0.11</td>\n",
       "      <td>15.81</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>HN148</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>89.06</td>\n",
       "      <td>74.30</td>\n",
       "      <td>89.06</td>\n",
       "      <td>74.30</td>\n",
       "      <td>97.19</td>\n",
       "      <td>8.13</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14.77</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>31.32</td>\n",
       "      <td>74.70</td>\n",
       "      <td>31.32</td>\n",
       "      <td>74.70</td>\n",
       "      <td>82.62</td>\n",
       "      <td>7.92</td>\n",
       "      <td>0.11</td>\n",
       "      <td>43.38</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>HN159</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>89.56</td>\n",
       "      <td>74.91</td>\n",
       "      <td>89.56</td>\n",
       "      <td>74.91</td>\n",
       "      <td>97.38</td>\n",
       "      <td>7.82</td>\n",
       "      <td>0.09</td>\n",
       "      <td>14.64</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>HN137</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>89.56</td>\n",
       "      <td>71.97</td>\n",
       "      <td>89.56</td>\n",
       "      <td>71.97</td>\n",
       "      <td>97.07</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.08</td>\n",
       "      <td>17.60</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>89.68</td>\n",
       "      <td>70.69</td>\n",
       "      <td>89.68</td>\n",
       "      <td>70.69</td>\n",
       "      <td>96.97</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.08</td>\n",
       "      <td>18.98</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>31.32</td>\n",
       "      <td>10.49</td>\n",
       "      <td>31.32</td>\n",
       "      <td>10.49</td>\n",
       "      <td>38.52</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.23</td>\n",
       "      <td>20.83</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>25.63</td>\n",
       "      <td>74.35</td>\n",
       "      <td>25.63</td>\n",
       "      <td>74.35</td>\n",
       "      <td>80.92</td>\n",
       "      <td>6.57</td>\n",
       "      <td>0.09</td>\n",
       "      <td>48.72</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>HN160</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>1</td>\n",
       "      <td>90.18</td>\n",
       "      <td>60.30</td>\n",
       "      <td>90.18</td>\n",
       "      <td>60.30</td>\n",
       "      <td>96.10</td>\n",
       "      <td>5.92</td>\n",
       "      <td>0.07</td>\n",
       "      <td>29.88</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>77.47</td>\n",
       "      <td>21.89</td>\n",
       "      <td>77.47</td>\n",
       "      <td>21.89</td>\n",
       "      <td>82.40</td>\n",
       "      <td>4.93</td>\n",
       "      <td>0.06</td>\n",
       "      <td>55.57</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>74.35</td>\n",
       "      <td>18.09</td>\n",
       "      <td>74.35</td>\n",
       "      <td>18.09</td>\n",
       "      <td>78.99</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0.06</td>\n",
       "      <td>56.26</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>52.35</td>\n",
       "      <td>92.68</td>\n",
       "      <td>52.35</td>\n",
       "      <td>92.68</td>\n",
       "      <td>96.51</td>\n",
       "      <td>3.83</td>\n",
       "      <td>0.04</td>\n",
       "      <td>40.34</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>92.68</td>\n",
       "      <td>95.90</td>\n",
       "      <td>92.68</td>\n",
       "      <td>95.90</td>\n",
       "      <td>99.70</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.04</td>\n",
       "      <td>3.22</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>52.63</td>\n",
       "      <td>92.86</td>\n",
       "      <td>52.63</td>\n",
       "      <td>92.86</td>\n",
       "      <td>96.62</td>\n",
       "      <td>3.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>40.23</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1</td>\n",
       "      <td>51.80</td>\n",
       "      <td>92.91</td>\n",
       "      <td>51.80</td>\n",
       "      <td>92.91</td>\n",
       "      <td>96.58</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0.04</td>\n",
       "      <td>41.11</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>74.70</td>\n",
       "      <td>10.49</td>\n",
       "      <td>74.70</td>\n",
       "      <td>10.49</td>\n",
       "      <td>77.35</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.04</td>\n",
       "      <td>64.21</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>92.91</td>\n",
       "      <td>32.12</td>\n",
       "      <td>92.91</td>\n",
       "      <td>32.12</td>\n",
       "      <td>95.19</td>\n",
       "      <td>2.28</td>\n",
       "      <td>0.02</td>\n",
       "      <td>60.78</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>92.68</td>\n",
       "      <td>23.58</td>\n",
       "      <td>92.68</td>\n",
       "      <td>23.58</td>\n",
       "      <td>94.41</td>\n",
       "      <td>1.73</td>\n",
       "      <td>0.02</td>\n",
       "      <td>69.10</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>74.35</td>\n",
       "      <td>97.69</td>\n",
       "      <td>74.35</td>\n",
       "      <td>97.69</td>\n",
       "      <td>99.41</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0.02</td>\n",
       "      <td>23.35</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>1</td>\n",
       "      <td>92.86</td>\n",
       "      <td>22.20</td>\n",
       "      <td>92.86</td>\n",
       "      <td>22.20</td>\n",
       "      <td>94.44</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.02</td>\n",
       "      <td>70.66</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>92.86</td>\n",
       "      <td>98.29</td>\n",
       "      <td>92.86</td>\n",
       "      <td>98.29</td>\n",
       "      <td>99.88</td>\n",
       "      <td>1.58</td>\n",
       "      <td>0.02</td>\n",
       "      <td>5.44</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>92.91</td>\n",
       "      <td>98.49</td>\n",
       "      <td>92.91</td>\n",
       "      <td>98.49</td>\n",
       "      <td>99.89</td>\n",
       "      <td>1.41</td>\n",
       "      <td>0.01</td>\n",
       "      <td>5.58</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  cluster_p  \\\n",
       "128   HN160        201  Epothilone B        302        PI-103          1   \n",
       "30    HN137       1007     Docetaxel       1010     Gefitinib          1   \n",
       "156   HN182        201  Epothilone B        302        PI-103          1   \n",
       "86    HN159       1007     Docetaxel       1010     Gefitinib          1   \n",
       "142   HN182       1007     Docetaxel       1010     Gefitinib          1   \n",
       "58    HN148       1007     Docetaxel       1010     Gefitinib          1   \n",
       "2     HN120       1007     Docetaxel       1010     Gefitinib          1   \n",
       "141   HN182       1007     Docetaxel        201  Epothilone B          1   \n",
       "152   HN182        133   Doxorubicin       1012    Vorinostat          1   \n",
       "68    HN148        133   Doxorubicin       1012    Vorinostat          1   \n",
       "113   HN160       1007     Docetaxel        201  Epothilone B          1   \n",
       "96    HN159        133   Doxorubicin       1012    Vorinostat          1   \n",
       "40    HN137        133   Doxorubicin       1012    Vorinostat          1   \n",
       "12    HN120        133   Doxorubicin       1012    Vorinostat          1   \n",
       "114   HN160       1007     Docetaxel       1010     Gefitinib          1   \n",
       "85    HN159       1007     Docetaxel        201  Epothilone B          1   \n",
       "124   HN160        133   Doxorubicin       1012    Vorinostat          1   \n",
       "153   HN182        201  Epothilone B       1010     Gefitinib          1   \n",
       "97    HN159        201  Epothilone B       1010     Gefitinib          1   \n",
       "57    HN148       1007     Docetaxel        201  Epothilone B          1   \n",
       "72    HN148        201  Epothilone B        302        PI-103          1   \n",
       "1     HN120       1007     Docetaxel        201  Epothilone B          1   \n",
       "29    HN137       1007     Docetaxel        201  Epothilone B          1   \n",
       "125   HN160        201  Epothilone B       1010     Gefitinib          1   \n",
       "41    HN137        201  Epothilone B       1010     Gefitinib          1   \n",
       "69    HN148        201  Epothilone B       1010     Gefitinib          1   \n",
       "100   HN159        201  Epothilone B        302        PI-103          1   \n",
       "13    HN120        201  Epothilone B       1010     Gefitinib          1   \n",
       "16    HN120        201  Epothilone B        302        PI-103          1   \n",
       "44    HN137        201  Epothilone B        302        PI-103          1   \n",
       "\n",
       "     cluster_kill_A  cluster_kill_B  kill_A  kill_B  kill_C  improve  \\\n",
       "128           74.70           68.43   74.70   68.43   92.01    17.31   \n",
       "30            51.80           32.12   51.80   32.12   67.28    15.49   \n",
       "156           77.47           81.65   77.47   81.65   95.86    14.22   \n",
       "86            25.63           18.09   25.63   18.09   39.08    13.46   \n",
       "142           42.14           21.89   42.14   21.89   54.81    12.67   \n",
       "58            52.35           23.58   52.35   23.58   63.58    11.24   \n",
       "2             52.63           22.20   52.63   22.20   63.14    10.52   \n",
       "141           42.14           77.47   42.14   77.47   86.96     9.49   \n",
       "152           86.77           70.95   86.77   70.95   96.16     9.39   \n",
       "68            89.06           74.30   89.06   74.30   97.19     8.13   \n",
       "113           31.32           74.70   31.32   74.70   82.62     7.92   \n",
       "96            89.56           74.91   89.56   74.91   97.38     7.82   \n",
       "40            89.56           71.97   89.56   71.97   97.07     7.51   \n",
       "12            89.68           70.69   89.68   70.69   96.97     7.30   \n",
       "114           31.32           10.49   31.32   10.49   38.52     7.20   \n",
       "85            25.63           74.35   25.63   74.35   80.92     6.57   \n",
       "124           90.18           60.30   90.18   60.30   96.10     5.92   \n",
       "153           77.47           21.89   77.47   21.89   82.40     4.93   \n",
       "97            74.35           18.09   74.35   18.09   78.99     4.64   \n",
       "57            52.35           92.68   52.35   92.68   96.51     3.83   \n",
       "72            92.68           95.90   92.68   95.90   99.70     3.80   \n",
       "1             52.63           92.86   52.63   92.86   96.62     3.76   \n",
       "29            51.80           92.91   51.80   92.91   96.58     3.67   \n",
       "125           74.70           10.49   74.70   10.49   77.35     2.65   \n",
       "41            92.91           32.12   92.91   32.12   95.19     2.28   \n",
       "69            92.68           23.58   92.68   23.58   94.41     1.73   \n",
       "100           74.35           97.69   74.35   97.69   99.41     1.71   \n",
       "13            92.86           22.20   92.86   22.20   94.44     1.59   \n",
       "16            92.86           98.29   92.86   98.29   99.88     1.58   \n",
       "44            92.91           98.49   92.91   98.49   99.89     1.41   \n",
       "\n",
       "     improve_p  sum_kill_dif            Combi Name 1            Combi Name 2  \\\n",
       "128       0.23          6.27     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "30        0.30         19.67     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "156       0.17          4.18     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "86        0.53          7.54     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "142       0.30         20.24     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "58        0.21         28.77     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "2         0.20         30.43     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "141       0.12         35.33  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "152       0.11         15.81  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "68        0.09         14.77  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "113       0.11         43.38  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "96        0.09         14.64  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "40        0.08         17.60  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "12        0.08         18.98  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "114       0.23         20.83     Docetaxel|Gefitinib     Gefitinib|Docetaxel   \n",
       "85        0.09         48.72  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "124       0.07         29.88  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin   \n",
       "153       0.06         55.57  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "97        0.06         56.26  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "57        0.04         40.34  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "72        0.04          3.22     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "1         0.04         40.23  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "29        0.04         41.11  Docetaxel|Epothilone B  Epothilone B|Docetaxel   \n",
       "125       0.04         64.21  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "41        0.02         60.78  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "69        0.02         69.10  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "100       0.02         23.35     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "13        0.02         70.66  Epothilone B|Gefitinib  Gefitinib|Epothilone B   \n",
       "16        0.02          5.44     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "44        0.01          5.58     Epothilone B|PI-103     PI-103|Epothilone B   \n",
       "\n",
       "                 Combi Name  \n",
       "128     Epothilone B|PI-103  \n",
       "30      Docetaxel|Gefitinib  \n",
       "156     Epothilone B|PI-103  \n",
       "86      Docetaxel|Gefitinib  \n",
       "142     Docetaxel|Gefitinib  \n",
       "58      Docetaxel|Gefitinib  \n",
       "2       Docetaxel|Gefitinib  \n",
       "141  Docetaxel|Epothilone B  \n",
       "152  Doxorubicin|Vorinostat  \n",
       "68   Doxorubicin|Vorinostat  \n",
       "113  Docetaxel|Epothilone B  \n",
       "96   Doxorubicin|Vorinostat  \n",
       "40   Doxorubicin|Vorinostat  \n",
       "12   Doxorubicin|Vorinostat  \n",
       "114     Docetaxel|Gefitinib  \n",
       "85   Docetaxel|Epothilone B  \n",
       "124  Doxorubicin|Vorinostat  \n",
       "153  Gefitinib|Epothilone B  \n",
       "97   Gefitinib|Epothilone B  \n",
       "57   Docetaxel|Epothilone B  \n",
       "72      Epothilone B|PI-103  \n",
       "1    Docetaxel|Epothilone B  \n",
       "29   Docetaxel|Epothilone B  \n",
       "125  Gefitinib|Epothilone B  \n",
       "41   Gefitinib|Epothilone B  \n",
       "69   Gefitinib|Epothilone B  \n",
       "100     Epothilone B|PI-103  \n",
       "13   Gefitinib|Epothilone B  \n",
       "16      Epothilone B|PI-103  \n",
       "44      Epothilone B|PI-103  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.sort_values('improve', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare for all lines and drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.702382Z",
     "start_time": "2020-11-17T13:34:44.687048Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "#pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.711420Z",
     "start_time": "2020-11-17T13:34:44.704000Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.745251Z",
     "start_time": "2020-11-17T13:34:44.713379Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>76.42</td>\n",
       "      <td>98.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>79.03</td>\n",
       "      <td>98.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>84.00</td>\n",
       "      <td>95.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>86.40</td>\n",
       "      <td>97.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>56.89</td>\n",
       "      <td>68.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  76.42                   98.29  \n",
       "121                  79.03                   98.49  \n",
       "122                  84.00                   95.90  \n",
       "123                  86.40                   97.69  \n",
       "124                  56.89                   68.43  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:44.749734Z",
     "start_time": "2020-11-17T13:34:44.747219Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.189884Z",
     "start_time": "2020-11-17T13:34:44.752285Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.48 (1.79e-03)\n",
      "Single drug [R-sq -59.90%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4E_single_drug_{}_bulk_{}.svg'.format(dosage_used, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.223212Z",
     "start_time": "2020-11-17T13:34:45.193428Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>85.90</td>\n",
       "      <td>99.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>94.32</td>\n",
       "      <td>99.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>91.27</td>\n",
       "      <td>99.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>92.54</td>\n",
       "      <td>99.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>65.67</td>\n",
       "      <td>92.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  85.90                   99.88  \n",
       "66   HN137                  94.32                   99.89  \n",
       "67   HN148                  91.27                   99.70  \n",
       "68   HN159                  92.54                   99.41  \n",
       "69   HN160                  65.67                   92.01  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.622401Z",
     "start_time": "2020-11-17T13:34:45.226450Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.34 (9.75e-02)\n",
      "Drug combination [R-sq -109.03%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_combi_drug_{}_bulk_{}.svg'.format(dosage_used, model_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.640477Z",
     "start_time": "2020-11-17T13:34:45.625025Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.652271Z",
     "start_time": "2020-11-17T13:34:45.642489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>patient</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combi Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>3.76</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.83</td>\n",
       "      <td>6.57</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>10.52</td>\n",
       "      <td>15.49</td>\n",
       "      <td>11.24</td>\n",
       "      <td>13.46</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>1.59</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4.64</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>1.58</td>\n",
       "      <td>1.41</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.71</td>\n",
       "      <td>17.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>7.30</td>\n",
       "      <td>7.51</td>\n",
       "      <td>8.13</td>\n",
       "      <td>7.82</td>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "patient                 HN120  HN137  HN148  HN159  HN160\n",
       "Combi Name                                               \n",
       "Docetaxel|Epothilone B   3.76   3.67   3.83   6.57   7.92\n",
       "Docetaxel|Gefitinib     10.52  15.49  11.24  13.46   7.20\n",
       "Gefitinib|Epothilone B   1.59   2.28   1.73   4.64   2.65\n",
       "Epothilone B|PI-103      1.58   1.41   3.80   1.71  17.31\n",
       "Doxorubicin|Vorinostat   7.30   7.51   8.13   7.82   5.92"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.672700Z",
     "start_time": "2020-11-17T13:34:45.654674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>64.20</td>\n",
       "      <td>71.45</td>\n",
       "      <td>50.30</td>\n",
       "      <td>52.19</td>\n",
       "      <td>37.40</td>\n",
       "      <td>84.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>69.45</td>\n",
       "      <td>70.71</td>\n",
       "      <td>68.32</td>\n",
       "      <td>78.14</td>\n",
       "      <td>23.30</td>\n",
       "      <td>65.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>78.23</td>\n",
       "      <td>77.29</td>\n",
       "      <td>84.78</td>\n",
       "      <td>45.46</td>\n",
       "      <td>30.06</td>\n",
       "      <td>65.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>86.18</td>\n",
       "      <td>94.34</td>\n",
       "      <td>90.95</td>\n",
       "      <td>92.48</td>\n",
       "      <td>66.34</td>\n",
       "      <td>83.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>74.99</td>\n",
       "      <td>78.81</td>\n",
       "      <td>76.18</td>\n",
       "      <td>81.57</td>\n",
       "      <td>35.26</td>\n",
       "      <td>88.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160  HN182\n",
       "Drug                                                            \n",
       "Docetaxel|Epothilone B  64.20  71.45  50.30  52.19  37.40  84.13\n",
       "Docetaxel|Gefitinib     69.45  70.71  68.32  78.14  23.30  65.20\n",
       "Doxorubicin|Vorinostat  78.23  77.29  84.78  45.46  30.06  65.83\n",
       "Epothilone B|PI-103     86.18  94.34  90.95  92.48  66.34  83.01\n",
       "Gefitinib|Epothilone B  74.99  78.81  76.18  81.57  35.26  88.09"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').mean()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:45.699233Z",
     "start_time": "2020-11-17T13:34:45.674657Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "    best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').mean()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.103729Z",
     "start_time": "2020-11-17T13:34:45.700862Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter_bulk.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.118682Z",
     "start_time": "2020-11-17T13:34:46.106369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>7.39</td>\n",
       "      <td>17.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.34</td>\n",
       "      <td>15.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>11.57</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>11.62</td>\n",
       "      <td>11.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>10.50</td>\n",
       "      <td>10.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>30.50</td>\n",
       "      <td>8.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.60</td>\n",
       "      <td>7.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>11.36</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>14.13</td>\n",
       "      <td>7.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>10.70</td>\n",
       "      <td>7.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-3.63</td>\n",
       "      <td>7.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>-7.97</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>5.86</td>\n",
       "      <td>5.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>14.99</td>\n",
       "      <td>4.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>-13.34</td>\n",
       "      <td>3.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>5.97</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>3.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>-6.06</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>12.54</td>\n",
       "      <td>1.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>6.03</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.37</td>\n",
       "      <td>1.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.76</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>15.30</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "19     Epothilone B|PI-103   HN160                       7.39   \n",
       "6      Docetaxel|Gefitinib   HN137                       7.34   \n",
       "8      Docetaxel|Gefitinib   HN159                      11.57   \n",
       "7      Docetaxel|Gefitinib   HN148                      11.62   \n",
       "5      Docetaxel|Gefitinib   HN120                      10.50   \n",
       "22  Doxorubicin|Vorinostat   HN148                      30.50   \n",
       "4   Docetaxel|Epothilone B   HN160                       4.60   \n",
       "23  Doxorubicin|Vorinostat   HN159                      11.36   \n",
       "21  Doxorubicin|Vorinostat   HN137                      14.13   \n",
       "20  Doxorubicin|Vorinostat   HN120                      10.70   \n",
       "9      Docetaxel|Gefitinib   HN160                      -3.63   \n",
       "3   Docetaxel|Epothilone B   HN159                      -7.97   \n",
       "24  Doxorubicin|Vorinostat   HN160                       5.86   \n",
       "13  Gefitinib|Epothilone B   HN159                      14.99   \n",
       "2   Docetaxel|Epothilone B   HN148                     -13.34   \n",
       "17     Epothilone B|PI-103   HN148                       5.97   \n",
       "0   Docetaxel|Epothilone B   HN120                      -1.41   \n",
       "1   Docetaxel|Epothilone B   HN137                      -6.06   \n",
       "14  Gefitinib|Epothilone B   HN160                       2.45   \n",
       "11  Gefitinib|Epothilone B   HN137                       1.30   \n",
       "12  Gefitinib|Epothilone B   HN148                      12.54   \n",
       "18     Epothilone B|PI-103   HN159                       6.03   \n",
       "10  Gefitinib|Epothilone B   HN120                       9.37   \n",
       "15     Epothilone B|PI-103   HN120                       9.76   \n",
       "16     Epothilone B|PI-103   HN137                      15.30   \n",
       "\n",
       "    Predicted % death increase  \n",
       "19                       17.31  \n",
       "6                        15.49  \n",
       "8                        13.46  \n",
       "7                        11.24  \n",
       "5                        10.52  \n",
       "22                        8.13  \n",
       "4                         7.92  \n",
       "23                        7.82  \n",
       "21                        7.51  \n",
       "20                        7.30  \n",
       "9                         7.20  \n",
       "3                         6.57  \n",
       "24                        5.92  \n",
       "13                        4.64  \n",
       "2                         3.83  \n",
       "17                        3.80  \n",
       "0                         3.76  \n",
       "1                         3.67  \n",
       "14                        2.65  \n",
       "11                        2.28  \n",
       "12                        1.73  \n",
       "18                        1.71  \n",
       "10                        1.59  \n",
       "15                        1.58  \n",
       "16                        1.41  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.126370Z",
     "start_time": "2020-11-17T13:34:46.120668Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.131192Z",
     "start_time": "2020-11-17T13:34:46.128419Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.490265Z",
     "start_time": "2020-11-17T13:34:46.133294Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RanksumsResult(statistic=0.8738275920531945, pvalue=0.38221215176158163) Ttest_indResult(statistic=1.2625799438723833, pvalue=0.2193918999490977)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (stats.ranksums(x, y), stats.ttest_ind(x, y))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_bulk.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.496937Z",
     "start_time": "2020-11-17T13:34:46.492767Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.87 (3.82e-01)\n"
     ]
    }
   ],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.510745Z",
     "start_time": "2020-11-17T13:34:46.498779Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 8, 1: 17})"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.533279Z",
     "start_time": "2020-11-17T13:34:46.520285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6, 0.6666666666666667, 0.5)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-17T13:34:46.725806Z",
     "start_time": "2020-11-17T13:34:46.535504Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8314980430197821\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.1)"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
